Abstract
With the rise of urban population, updated spatial information of indoor environments is needed in a growing number of applications. Navigational assistance for disabled or aged people, guidance for robots, augmented reality for gaming, and tourism or training emergency assistance units are just a few examples of the emerging applications requiring real three-dimensional (3D) spatial data of indoor scenes. This work proposes the use of point clouds for obstacle-aware indoor pathfinding. Point clouds are firstly used for reconstructing semantically rich 3D models of building structural elements in order to extract initial navigational information. Potential obstacles to navigation are classified in the point cloud and directly used to correct the path according to the mobility skills of different users. The methodology is tested in several real case studies for wheelchair and ordinary users. Experiments show that, after several iterations, paths are readapted to avoid obstacles.
Highlights
Over the last few years, indoor navigation became a subject of research interest because people spend a considerable amount of their time in indoor spaces such as houses, office buildings, commercial centers, and transportation facilities, among others
The navigable space is extracted from the surfaces representing floors and doors, and initial paths are updated when obstacles are detected in the point cloud, and when they are considered to interrupt the navigation process
In terms of indoor navigation, a methodology extracting topological relationships between the spaces of an 3D indoor environment modeled from point clouds was recently presented by References [18,29]
Summary
Over the last few years, indoor navigation became a subject of research interest because people spend a considerable amount of their time in indoor spaces such as houses, office buildings, commercial centers, and transportation facilities, among others. Local authorities are increasingly being required to make accessibility diagnoses and to take corrective actions in public spaces for enabling navigation for disabled people [6] Building crisis management, such as fire protection or planned terrorist attacks, is another application where planned paths can provide possible safe and efficient evacuation paths under different emergency conditions [7]. Successful pathfinding for a 3D indoor environment depends on the accurate and updated geometry, semantics, and topology of building components and spaces [8]. The navigable space is extracted from the surfaces representing floors and doors, and initial paths are updated when obstacles are detected in the point cloud, and when they are considered to interrupt the navigation process.
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